The predictors trained on the parallel corpus will serve as feature extractors for the quality estimation task.
It should be noted that there exists discrepancy between the machine translation task and the quality estimation task due to the fact that unlike translation datasets, the target sentences in QE datasets are not the golden references with few translation errors.
The mistakes contained in translated sentences are necessary for the learning of the quality estimation models to distinguish between good and bad translations.
Due to this discrepancy, directly using the hidden representations outputted from the trained predictors would not be the optimal solution.
In our design, we follow the idea of previous work [1]} to include mis-matching features and model derived features, and propose a new design of dual model features based on the model-level dual learning.
